Fall 2026 · CRN 21732
This repository is the course hub for DATA 201. Weekly materials, labs, readings, and assignment notes live in the folders linked below.
Official syllabus: DATA 201 Fall 2026 Syllabus (CRN 21732)
The schedule is tentative and may be adjusted as the semester progresses. Updates will be announced in class, on Blackboard, and in Microsoft Teams.
| Instructor | Dr. Rebin Abdulkader Muhammad |
| Rebin.Muhammad@montgomerycollege.edu | |
| Office phone | 240-567-1931 |
| Office hours | Every day, 12:00–1:00 PM via Microsoft Teams |
| Course | DATA 201 ZRA — Statistical Methods in Data Science |
| Meeting | Thursday, 6:00–8:55 PM (online, Microsoft Teams) |
| First meeting | Thursday, September 3, 2026 |
| Last meeting | Thursday, December 17, 2026 |
| Prerequisite | C or better in DATA 101, or consent of department |
Course description. Statistical concepts and applications related to data science, including advanced exploratory data analysis, nonparametric inference and simulation for larger datasets, logistic regression modeling, statistical programming, and basics of machine learning.
| 📁 Resources | 📁 Project 1 | 📁 Project 2 | 📁 Final Project |
|---|
Textbooks in this repo
- An Introduction to Statistical Learning with Python (ISLP)
- Introduction to Machine Learning with Python, Chapter 1 (Müller & Guido)
- GitHub project repository setup guide
ISLP is also available as a free PDF from the authors' website.
Machine learning and statistical learning foundations
- Inference vs. prediction
- Supervised vs. unsupervised learning
- Regression vs. classification
- Problem framing and tradeoffs
Resources / assignment: ISLP Ch. 1–2; Google Colab introduction; short problem-framing activity
Data preparation and reproducible workflows
- Data quality and missing values
- Categorical variables, scaling, and transformations
- Data leakage
Resources / assignment: ISLP Ch. 2 lab; Müller & Guido Ch. 4; preprocessing lab
Logistic regression and classification foundations
- Probabilities and decision boundaries
- Classification vs. inference
- Loss / objective ideas
Resources / assignment: ISLP Ch. 4; logistic regression Colab lab
Classification algorithms
- Logistic regression, LDA, QDA, and KNN
- Model comparison
Resources / assignment: ISLP Ch. 4; Müller & Guido Ch. 2; classifier comparison lab
Model evaluation
- Train/test split
- Confusion matrix, accuracy, precision, recall, specificity, F1
- ROC-AUC and thresholds
Resources / assignment: ISLP Ch. 5; Müller & Guido Ch. 5; evaluation metrics lab
Cross-validation and model selection
- Overfitting and underfitting
- Hyperparameter tuning
- Model selection
Project 1 due
Resources / assignment: ISLP Ch. 5; Müller & Guido Ch. 5
Feature engineering and modeling pipelines
- Encoding, scaling, and transformations
- Feature selection
- Reproducible scikit-learn pipelines
Resources / assignment: Müller & Guido Ch. 3, 4, and 6; pipeline-building lab
Regularization and model complexity
- Ridge and lasso
- Bias–variance tradeoff
- Interpretability vs. flexibility
Resources / assignment: ISLP Ch. 6; regularization lab
Decision trees
- Splitting, impurity, and pruning
- Classification and regression trees
- Interpretation
Resources / assignment: ISLP Ch. 8; Müller & Guido Ch. 2; decision-tree lab
Ensemble methods
- Bagging, random forests, and boosting
- Variable importance
Resources / assignment: ISLP Ch. 8; Müller & Guido Ch. 2; ensemble comparison lab
End-to-end model comparison
- Pipelines and tuning
- Comparing logistic / regularized models with trees and ensembles
Project 2 due
Resources / assignment: Integrated ISLP + Müller & Guido workflow
Neural networks
- Perceptron, hidden layers, and activation functions
- Loss functions, gradient descent, and backpropagation intuition
Resources / assignment: ISLP Ch. 10; Müller & Guido neural-network section; introductory NN lab
College closed — no class
Introductory deep learning
- Deeper networks
- Regularization and validation
- Practical applications
Resources / assignment: ISLP Ch. 10; deep-learning Colab lab / discussion
Responsible and reproducible ML
- Bias, data quality, and drift
- Security, accountability, and model limitations
Final Project workshop
Resources / assignment: Selected textbook examples; model-risk discussion
Final Project
Final Project due
| Course activity | Weight |
|---|---|
| Project 1 | 15% |
| Project 2 | 15% |
| Lab / homework | 20% |
| Class discussion | 10% |
| Museum visit | 5% |
| Data science infographic | 5% |
| Final project | 30% |
- Fall classes begin: August 31, 2026
- First DATA 201 meeting: Thursday, September 3, 2026
- Labor Day (college closed): September 7, 2026
- Refund date: September 9, 2026
- No grade / change to audit deadline: September 23, 2026
- MC Day (no classes for students): October 20, 2026
- Last date to withdraw: November 18, 2026
- Fall Break (college closed): November 25–29, 2026
- Final exam week: December 14–20, 2026
- Last DATA 201 meeting / Final Project due: Thursday, December 17, 2026
- Official end of semester: December 20, 2026
- Weekly folders will include notebooks, slides, datasets, and instructions as they are posted.
- Lab and homework details, due dates, and submission locations will be posted on Blackboard. Selected assignments may be submitted through GitHub.
- Project requirements, due dates, and rubrics will be posted on Blackboard and in the project folders.
- Students are responsible for confirming that submitted work uploaded successfully.
Last updated: Fall 2026